Scene text detection by adaptive feature selection with text scale-aware loss

Abstract

Since convolutional neural networks(CNNs) were applied to scene text detection, the accuracy of text detection has been improved a lot. However, limited by the receptive fields of regular CNNs and due to the large scale variations of texts in images, current text detection methods may fail to detect some texts well when dealing with more challenging text instances, such as arbitrarily shaped texts and extremely small texts. In this paper, we propose a new segmentation based scene text detector, which is equipped with deformable convolution and global channel attention. In order to detect texts of arbitrary shapes, our method replaces traditional convolutions with deformable convolutions, the sampling locations of deformable convolutions are deformed with augmented offsets so that it can better adapt to any shapes of texts, especially curved texts. To get more representative features for texts, an Adaptive Feature Selection module is introduced to better exploit text content through global channel attention. Meanwhile, a scale-aware loss, which adjusts the weights of text instances with different sizes, is formulated to solve the text scale variation problem. Experiments on several standard benchmarks, including ICDAR2015, SCUT-CTW1500, ICDAR2017-MLT and MSRA-TD500 verify the superiority of the proposed method.